Xinyao Zhang
Papers
9
Total Citations
114
H-Index
4
About
Xinyao Zhang is a leading researcher at the intersection of robotics, artificial intelligence, and sustainable manufacturing, with a primary focus on human–robot collaboration (HRC) for disassembly and remanufacturing. Their work addresses critical challenges in automating the processing of end-of-life electronics, a domain where uncertainty and complexity have traditionally limited robotic adoption. Zhang’s key contributions include developing an automatic screw detection and tool recommendation system for robotic disassembly (41 citations), which directly improves efficiency in remanufacturing plants. They have also pioneered unsupervised human activity recognition for disassembly tasks (36 citations), enabling robots to learn from human demonstrations without labeled data. A standout achievement is their use of Transformer networks for the early prediction of human intention in collaborative tasks (20 citations), a breakthrough that enhances both safety and workflow fluidity. Zhang has further advanced the field through multi-task learning frameworks that simultaneously predict human intention and trajectory, and through RoboGPT, an intelligent agent leveraging large language models for long-term embodied task planning. With a growing citation record and a clear trajectory toward smarter, safer human–robot teams, Zhang’s research is shaping the future of intelligent automation in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
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- 2Unsupervised Human Activity Recognition Learning for Disassembly Tasks36 citations · 2023
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- 5Laser slam-based autonomous navigation for fire patrol robots4 citations · 2023
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